In this paper, we present the first comprehensive library for graph outlier detection, called PyGOD. Uniquely, it supports a wide range of detection algorithms with unified APIs, rich documentation, and robust code design, which are readily useful for both academic research and industry applications.
The development plan of PyGOD will focus on multiple aspects: (i) including more algorithms for different sub-tasks, e.g., outlier detection in edges and sub-graphs;
(ii) collaborating with industries to make PyGOD more practical and tailor the needs of practitioners;
(iii) optimizing its accessibility and scalability with the latest advancement in graph [1]}; and
(iv) incorporate automated machine learning to enable intelligent model selection and hyperparameter tuning [2]}.
